87 citations · 141 across the 46 of their papers we have counts for
27 papers · 1 filter
From Coarse to Continuous: Progressive Refinement Implicit Neural Representation for Motion-Robust Anisotropic MRI Reconstruction
Zhenxuan Zhang, Lipei Zhang, Yanqi Cheng +10
In motion-robust magnetic resonance imaging (MRI), slice-to-volume reconstruction is critical for recovering anatomically consistent 3D brain volumes from 2D slices, especially und…
Enhancing Diffusion-Weighted Images (DWI) for Diffusion MRI: Is it Enough without Non-Diffusion-Weighted B=0 Reference?
Yinzhe Wu, Jiahao Huang, Fanwen Wang +4
Diffusion MRI (dMRI) is essential for studying brain microstructure, but high-resolution imaging remains challenging due to the inherent trade-offs between acquisition time and sig…
RSFR: A Coarse-to-Fine Reconstruction Framework for Diffusion Tensor Cardiac MRI with Semantic-Aware Refinement
Jiahao Huang, Fanwen Wang, Pedro F. Ferreira +14
Cardiac diffusion tensor imaging (DTI) offers unique insights into cardiomyocyte arrangements, bridging the gap between microscopic and macroscopic cardiac function. However, its c…
Task-oriented Uncertainty Collaborative Learning for Label-Efficient Brain Tumor Segmentation
Zhenxuan Zhang, Hongjie Wu, Jiahao Huang +5
Multi-contrast magnetic resonance imaging (MRI) plays a vital role in brain tumor segmentation and diagnosis by leveraging complementary information from different contrasts. Each…
Pretext Task Adversarial Learning for Unpaired Low-field to Ultra High-field MRI Synthesis
Zhenxuan Zhang, Peiyuan Jing, Coraline Beitone +4
Given the scarcity and cost of high-field MRI, the synthesis of high-field MRI from low-field MRI holds significant potential when there is limited data for training downstream tas…
Lightweight Hypercomplex MRI Reconstruction: A Generalized Kronecker-Parameterized Approach
Haosen Zhang, Jiahao Huang, Yinzhe Wu +4
Magnetic Resonance Imaging (MRI) is crucial for clinical diagnostics but is hindered by prolonged scan times. Current deep learning models enhance MRI reconstruction but are often…
Towards Modality- and Sampling-Universal Learning Strategies for Accelerating Cardiovascular Imaging: Summary of the CMRxRecon2024 Challenge
Fanwen Wang, Zi Wang, Yan Li +60
Cardiovascular health is vital to human well-being, and cardiac magnetic resonance (CMR) imaging is considered the {clinical reference standard} for diagnosing cardiovascular disea…
Explicit Differentiable Slicing and Global Deformation for Cardiac Mesh Reconstruction
Yihao Luo, Dario Sesia, Fanwen Wang +9
Mesh reconstruction of the cardiac anatomy from medical images is useful for shape and motion measurements and biophysics simulations to facilitate the assessment of cardiac functi…
Learning Task-Specific Sampling Strategy for Sparse-View CT Reconstruction
Liutao Yang, Jiahao Huang, Yingying Fang +4
Sparse-View Computed Tomography (SVCT) offers low-dose and fast imaging but suffers from severe artifacts. Optimizing the sampling strategy is an essential approach to improving th…
Diff3Dformer: Leveraging Slice Sequence Diffusion for Enhanced 3D CT Classification with Transformer Networks
Zihao Jin, Yingying Fang, Jiahao Huang +3
The manifestation of symptoms associated with lung diseases can vary in different depths for individual patients, highlighting the significance of 3D information in CT scans for me…
Low-rank based motion correction followed by automatic frame selection in DT-CMR
Fanwen Wang, Pedro F. Ferreira, Camila Munoz +8
Motivation: Post-processing of in-vivo diffusion tensor CMR (DT-CMR) is challenging due to the low SNR and variation in contrast between frames which makes image registration diffi…
Anatomy-guided fiber trajectory distribution estimation for cranial nerves tractography
Lei Xie, Qingrun Zeng, Huajun Zhou +6
Diffusion MRI tractography is an important tool for identifying and analyzing the intracranial course of cranial nerves (CNs). However, the complex environment of the skull base le…
MambaMIR: An Arbitrary-Masked Mamba for Joint Medical Image Reconstruction and Uncertainty Estimation
Jiahao Huang, Liutao Yang, Fanwen Wang +5
The recent Mamba model has shown remarkable adaptability for visual representation learning, including in medical imaging tasks. This study introduces MambaMIR, a Mamba-based model…
High-Resolution Reference Image Assisted Volumetric Super-Resolution of Cardiac Diffusion Weighted Imaging
Yinzhe Wu, Jiahao Huang, Fanwen Wang +4
Diffusion Tensor Cardiac Magnetic Resonance (DT-CMR) is the only in vivo method to non-invasively examine the microstructure of the human heart. Current research in DT-CMR aims to…
Style Transfer and Self-Supervised Learning Powered Myocardium Infarction Super-Resolution Segmentation
Lichao Wang, Jiahao Huang, Xiaodan Xing +7
This study proposes a pipeline that incorporates a novel style transfer model and a simultaneous super-resolution and segmentation model. The proposed pipeline aims to enhance diff…
Efficient Post-processing of Diffusion Tensor Cardiac Magnetic Imaging Using Texture-conserving Deformable Registration
Fanwen Wang, Pedro F. Ferreira, Yinzhe Wu +8
Diffusion tensor cardiac magnetic resonance (DT-CMR) is a method capable of providing non-invasive measurements of myocardial microstructure. Image registration is essential to cor…
CDiffMR: Can We Replace the Gaussian Noise with K-Space Undersampling for Fast MRI?
Jiahao Huang, Angelica Aviles-Rivero, Carola-Bibiane Schönlieb +1
Deep learning has shown the capability to substantially accelerate MRI reconstruction while acquiring fewer measurements. Recently, diffusion models have gained burgeoning interest…
Swin Deformable Attention Hybrid U-Net for Medical Image Segmentation
Lichao Wang, Jiahao Huang, Xiaodan Xing +1
Medical image segmentation is a crucial task in the field of medical image analysis. Harmonizing the convolution and multi-head self-attention mechanism is a recent research focus…
ViGU: Vision GNN U-Net for Fast MRI
Jiahao Huang, Angelica Aviles-Rivero, Carola-Bibiane Schonlieb +1
Deep learning models have been widely applied for fast MRI. The majority of existing deep learning models, e.g., convolutional neural networks, work on data with Euclidean or regul…
CS: A Controllable and Simultaneous Synthesizer of Images and Annotations with Minimal Human Intervention
Xiaodan Xing, Jiahao Huang, Yang Nan +5
The destitution of image data and corresponding expert annotations limit the training capacities of AI diagnostic models and potentially inhibit their performance. To address such…
Data and Physics Driven Learning Models for Fast MRI -- Fundamentals and Methodologies from CNN, GAN to Attention and Transformers
Jiahao Huang, Yingying Fang, Yang Nan +9
Research studies have shown no qualms about using data driven deep learning models for downstream tasks in medical image analysis, e.g., anatomy segmentation and lesion detection,…
Fast MRI Reconstruction: How Powerful Transformers Are?
Jiahao Huang, Yinzhe Wu, Huanjun Wu +1
Magnetic resonance imaging (MRI) is a widely used non-radiative and non-invasive method for clinical interrogation of organ structures and metabolism, with an inherently long scann…
Swin Transformer for Fast MRI
Jiahao Huang, Yingying Fang, Yinzhe Wu +6
Magnetic resonance imaging (MRI) is an important non-invasive clinical tool that can produce high-resolution and reproducible images. However, a long scanning time is required for…
Edge-Enhanced Dual Discriminator Generative Adversarial Network for Fast MRI with Parallel Imaging Using Multi-view Information
Jiahao Huang, Weiping Ding, Jun Lv +7
In clinical medicine, magnetic resonance imaging (MRI) is one of the most important tools for diagnosis, triage, prognosis, and treatment planning. However, MRI suffers from an inh…